導入商業智慧系統監測積極營養介入對於癌症患者之影響

Applying Power (Business Intelligence) BI to monitor the effect of active nutrition intervention on cancer patients.

陳盈汝(Ying-Ru Chen)

指導教授 ： 林志立

共同指導教授 ： 黃建寧(Chien-Ning Huang)

中山醫學大學/醫學院/醫學研究所/碩士(2021年)

https://doi.org/10.6834/csmu202100183

摘要

癌症是目前全球最重要的公共衛生議題之一，根據衛生福利部2019年統計，癌症蟬聯台灣十大死因之首已超過38年。癌症病人因疾病本身與治療易有營養攝取不足、營養不良、體重減輕之情況發生，及早提供營養介入以改善癌症病人的生活品質、延緩疾病惡化及併發症產生，對於整體醫療有正面輔助之效果。本研究的目的為導入商業智慧系統 (Business intelligence, BI) 監測某醫學中心於2020年之全癌別病人，經積極營養介入後，對於癌症患者的影響。
本研究將癌症營養照護資料導入於商業智慧系統中，資料庫自動更新且匯入BI進行分析可即時且快速的獲取所需資料，並可隨著不同的監測需求，彈性變動不同統計方式及結果，以監測癌症總體照護品質。納入對象為經醫師診斷為癌症 (ICD C00.0-C96.9, D00.0-D09.9)，且接受營養介入之住院病人，並採用初次和出院前接受營養治療的記錄進行分析；營養介入狀況、體重變化及生化值於營養介入前後以Wilcoxon signed rank test統計分析，營養介入與住院天數則以Mann-Whitney u test統計分析。
在這項研究中，納入2261個案，接受營養介入的三大癌症是消化器官、頭頸癌和呼吸系統及胸腔內器官；男性有1310位 (52.9%)，女性有951位 (42.1%)，平均BMI 20.5 ± 5.4，PG-SGA score 9.2 ± 4.2，平均住院天數為13.2 ± 14.8天，結果顯示癌症病人營養介入前後的熱量攝取達每公斤體重20.79 (6.51-28.86) 大卡提升至26.67 (19.90-32.33) 大卡，蛋白質攝取由每公斤0.83 (0.35-1.17)克提升至1.07 (0.81-1.33)克，體重變化由51.35 (44.00-61.00) 公斤提升至51.40 (44.00-61.00)公斤，且統計後皆達顯著差異 (P < 0.05)。另外在營養相關指標生化數值Prealbumin於營養介入前後統計上亦達顯著差異 (P < 0.05)；進一步分析每公斤體重熱量蛋白質達25大卡及1.2克蛋白質與未達者相比可顯著縮短癌症治療期間之住院天數 (P < 0.05)。
因此，透過資訊化導入，監測住院癌症病人接受積極營養介入之照護品質，可及時加強熱量蛋白質攝取不足之患者之營養治療，以避免體重減輕，改善營養狀況和臨床結果。

關鍵字

癌症 ； 營養治療 ； 商業智慧系統 ； 營養介入 ； 住院天數

並列摘要

Background: Cancer is a major issue of the most important public health worldwide currently. According to statistics from the Taiwan Ministry of Health and Welfare in 2019, cancer has been the top ten causes of death in Taiwan for more than 38 years. Cancer patients are often encountered inadequate nutritional intake, malnutrition, and weight loss due to the disease and treatment. Providing early nutritional therapy for cancer patients can improve their quality of life, delay the deterioration of the disease and the occurrence of complications, and has positive auxiliary effects on overall medical treatment.
Objective: The aim of this study was to apply Business Intelligence (BI) in monitoring the effect of active nutrition intervention on all type of cancer patients in a medical center in 2020.
Methods and Materials: This retrospective cohort study made use of data collection from Business Intelligence included patients were diagnosed with cancer (ICD C00.0-C96.9, D00.0-D09.9) and accepted nutrition therapy by dietitians. The nutrition intervention records of first and last were used for analysis. These real-time data can be flexibly changed according to different monitoring requirements and different statistical methods which is automatically updated and imported into sharepoint for analysis to monitor the overall quality of nutrition care of cancer patients. The status of nutrition, weight changes and biochemical measurements before and after nutrition therapy were statistically analyzed by Wilcoxon signed rank test, and different nutrition achievement and length of hospital stay (LOS) were statistically analyzed by Mann-Whitney u test.
Results: Results in this study, the top three cancer sites for nutrition care were digestive organs, head and neck cancer and respiratory and intrathoracic organs. There were 1310 males (52.9%) and 951 females (42.1%). Average BMI was 20.5 ± 5.4, the PG-SGA score was 9.2 ± 4.2, and the average LOS was 13.2 ± 14.8 days. We found that the calorie intake of cancer patients before and after nutritional intervention increased from 20.79 (6.51-28.86) kcal/KgBW to 26.67 (19.90-32.33) kcal/KgBW (P <0.05), and protein intake increased from 0.83 (0.35-1.17) g/Kg to 1.07 (0.81) -1.33) g/Kg (P <0.05), the weight change increased from 51.35 (44.00-61.00) kg to 51.40 (44.00-61.00) kg (P <0.05). In addition, significant difference in prealbumin before and after nutrition intervention (P <0.05); Further analysis of the caloric protein per kilogram body weight of 25 calories and 1.2 grams of protein compared with those who did not reach significantly reduced the length of hospital stays (LOS) during cancer treatment (P <0.05).
Conclusion: Therefore, monitoring the quality of care for cancer patients receiving active nutrition interventions through applying of Business intelligence can promptly strengthen the nutrition therapy of patients with insufficient calorie and protein intake to avoid weight loss, improve nutrition status and clinical results.

並列關鍵字

cancer ； nutrition therapy ； Business Intelligence (BI) ； nutrition intervention ； length of hospital stays (LOS)

參考文獻

Arends, J., P. Bachmann, V. Baracos, N. Barthelemy, H. Bertz, F. Bozzetti, K. Fearon, E. Hutterer, E. Isenring, S. Kaasa, Z. Krznaric, B. Laird, M. Larsson, A. Laviano, S. Muhlebach, M. Muscaritoli, L. Oldervoll, P. Ravasco, T. Solheim, F. Strasser, M. de van der Schueren J. C. Preiser (2017a) ESPEN guidelines on nutrition in cancer patients. Clin Nutr, 36, 11-48.

Arends, J., V. Baracos, H. Bertz, F. Bozzetti, P. C. Calder, N. E. P. Deutz, N. Erickson, A. Laviano, M. P. Lisanti, D. N. Lobo, D. C. McMillan, M. Muscaritoli, J. Ockenga, M. Pirlich, F. Strasser, M. de van der Schueren, A. Van Gossum, P. Vaupel A. Weimann (2017b) ESPEN expert group recommendations for action against cancer-related malnutrition. Clin Nutr, 36, 1187-1196.

Arnold, C. M. P. Richter (1989) The effect of oral nutritional supplements on head and neck cancer. Int J Radiat Oncol Biol Phys, 16, 1595-9.

Ashrafi, N., L. Kelleher J.-P. Kuilboer (2014) The Impact of Business Intelligence on Healthcare Delivery in the USA. IJIKM, 9, 117-130.

August, D. A. M. B. Huhmann (2009) A.S.P.E.N. clinical guidelines: nutrition support therapy during adult anticancer treatment and in hematopoietic cell transplantation. JPEN J Parenter Enteral Nutr, 33, 472-500.